Computer readable storage medium, apparatus, and method for image processing

ABSTRACT

A non-transitory computer readable storage medium that stores a program for image processing that causes a computer to execute a process including: detecting a change in a lung based on a plurality of images of the chest scanned at different times, the change in the lung indicating that each position of each part of the lung changes toward a specified position of the lung, and outputting the change of the lung.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2015-147040, filed on Jul. 24,2015, the entire contents of which are incorporated herein by reference.

FIELD

The embodiments disclosed herein relate to an image processing program,an image processing apparatus, and an image processing method.

BACKGROUND

In job sites of medical care, a radiologist uses computed tomography(CT) scanned images at different times to compare locations of illnessor locations at which there is a suspicion of illness with each other tomake a determination of illness of a patient.

CITATION LIST

[Patent Document 1] Japanese Laid-open Patent Publication No.2013-141603

SUMMARY

According to an aspect of the invention, a non-transitory computerreadable storage medium that stores a program for image processing thatcauses a computer to execute a process includes detecting a change in alung based on a plurality of images of the chest scanned at differenttimes, the change in the lung indicating that each position of each partof the lung changes toward a specified position of the lung, andoutputting the change of the lung.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a view depicting an example of a CT imaging system;

FIG. 2 is a view depicting a hardware configuration of an imageprocessing apparatus;

FIG. 3 is a view illustrating a relationship among process contents of adiagnosis support unit of an image processing apparatus, operationcontents of a health care worker, and display contents of a paralleldisplay screen image;

FIG. 4 is a view illustrating a relationship among process contents of adiagnosis support unit of an image processing apparatus, operationcontents of a health care worker, and display contents of a paralleldisplay screen image;

FIG. 5 is a view illustrating an example of information stored in animage database (DB);

FIG. 6 is a view depicting factors of a fluctuation of a local positionof a comparison destination CT image with respect to a comparison sourceCT image;

FIGS. 7A to 7C depict views more particularly illustrating a fluctuationof a position based on a change of a tumor;

FIGS. 8A to 8D depict views illustrating a calculation process of arepresentative vector and a calculation process of a correspondingregion;

FIG. 9 is a view depicting an image for which local positioning has beenperformed using a representative vector including an influence ofnon-rigid deformation;

FIG. 10 is a view depicting a functional configuration of a secondregistration unit;

FIG. 11 is a view illustrating process contents of a convergence regiondecision unit;

FIGS. 12A and 12B depict views illustrating a calculation method of arepresentative vector when it is decided that there exists a convergenceregion;

FIGS. 13A and 13B depict views illustrating a calculation method of arepresentative vector when it is decided that there is no convergenceregion;

FIG. 14 is a view depicting an image obtained by performing localpositioning using a representative vector from which an influence ofnon-rigid deformation has been removed;

FIGS. 15A to 15C-2 depict views illustrating a particular example of animage processing process executed by an image processor;

FIG. 16 is a first flow chart illustrating a process executed by asecond registration unit;

FIG. 17 is a flow chart of a convergence region decision process;

FIGS. 18A and 18B depict flow charts of a local positioning process;

FIG. 19 is a flow chart of an image processing process based on atime-dependent variation;

FIG. 20 is a second view illustrating a relationship among processcontents of a diagnosis support unit of an image processing apparatus,operation contents of a health care worker, and display contents of aparallel display screen image;

FIG. 21 is a second flow chart of a process executed by a secondregistration unit;

FIG. 22 depict views illustrating a process for scanning a comparisonsource CT image using region of interest (ROI) candidates to determinean ROI;

FIG. 23 is a third flow chart of a process executed by a secondregistration unit;

FIG. 24 is the third flow chart of a process executed by the secondregistration unit;

FIG. 25 is a third view illustrating a relationship among processcontents of a diagnosis support unit of an image processing apparatus,operation contents of a health care worker, and display contents of aparallel display screen image;

FIG. 26 depict views illustrating a manner in which a display mode of anenlarged display screen image is changed by a display controller; and

FIG. 27 is a flow chart of a process executed by a display controller.

DESCRIPTION OF EMBODIMENTS

Incidentally, where a patient has a tumor (for example, adenocarcinoma)in the lung, alveoli are collapsed by the adenocarcinoma, andsurrounding such as blood vessels move so as to converge to thecollapsed location. While, in a CT image, a tumor or a blood vessel isdisplayed white, since the lung is deformed by an influence of thebreathing or the heart beating of the patient, it is difficult for aradiologist who is not experienced highly to find, on the basis of CTscanned images (or captured image) of the chest, whether or not thereexists a tumor or whether or not some blood vessel is dislocated.

Therefore, it is desirable to reduce the load imposed on a radiologistwhen the radiologist makes a determination of a location at which somevascular convergence by an influence of alveoli collapsed due to a tumorfrom images of the lung deformed by an influence of the breathing or theheart beating.

In the following, embodiments are described with reference to theaccompanying drawings. It is to be noted that, in the presentspecification and the accompanying drawings, like element havingsubstantially like functional configurations are denoted by likereference characters and overlapping description of them is omittedherein to avoid redundancy.

First Embodiment

First, a computed tomography (CT) imaging system including an imageprocessing apparatus according to a first embodiment is described. FIG.1 is a view depicting an example of a CT imaging system.

A CT imaging system 100 includes a CT apparatus 110, an image processingapparatus 120, and an image database (database is hereinafter referredto as DB) 130. The CT apparatus 110 and the image processing apparatus120 are electrically coupled to each other such that transfer of data isperformed between the two apparatus. Also the image processing apparatus120 and the image DB 130 are electrically coupled to each other suchthat transfer of data is performed between the two apparatus.

The CT apparatus 110 scans the inside of the body of a patient utilizingradiations and uses a computer to perform processing to generate CTimages that are slice images of the patient (such a process as justdescribed is hereinafter referred to as “to CT scan images”). The CTapparatus 110 transmits CT scanned images to the image processingapparatus 120.

The image processing apparatus 120 stores CT scanned images by the CTapparatus 110 into the coupled image DB 130. Further, the imageprocessing apparatus 120 processes the CT scanned images by the CTapparatus 110 and displays the processed CT images to medial staff suchas a radiologist. It is to be noted that the image processing apparatus120 functions as a diagnosis support unit 140 to perform such processesas described above when a diagnosis support program, which is an exampleof an image processing program installed therein, is executed by acomputer.

The image DB 130 receives CT scanned images by the CT apparatus 110through the image processing apparatus 120 and stores the CT imagesseparately for pluralities of CT scanned images (scanned image series orscanned image group) at a same time.

The diagnosis support unit 140 is a function that is utilized when ahealth care worker makes a diagnosis of a patient on the basis of the CTscanned images by the CT apparatus 110 and stored in the image DB 130.The diagnosis support unit 140 displays CT scanned images, for example,at different times in parallel such that the health care worker candiagnose through comparison of the CT images. It is to be noted that oneof CT images displayed in parallel (for example, a CT scanned imagebefore lapse of a given period of time) is referred to as “comparisonsource CT image” and another one of the CT images (for example, a CTscanned image after lapse of the given period of time) is referred to as“comparison destination CT image.”

The diagnosis support unit 140 displays an image in a given region(region of interest (ROI)) including a position designated by a healthcare worker in the comparison source CT image in an enlarged scale on anenlarged display screen image. Further, the diagnosis support unit 140extracts an image in a corresponding region corresponding to the givenregion including the designated position from within the comparisondestination CT image and displays the extracted image in an enlargedscale in the enlarged display screen image. In this manner, with thediagnosis support unit 140, since an image in a given region including adesignated position and an image of a corresponding region areautomatically displayed in an enlarged scale, it is possible to reducethe load of the diagnosis on the health care worker and reduce the laborof the health care worker for an operation for displaying an enlargedimage.

It is to be noted that, in order to execute such processes as describedabove, the diagnosis support unit 140 includes a first registration unit141, a second registration unit 142, and a display controller 143.

The first registration unit 141 is implemented, for example, by a firstregistration program executed by a computer. The first registration unit141 corrects, when CT scanned images at different times are displayed inparallel, positional displacement between the CT images by using affinetransformation to perform global positioning between the CT images.

The second registration unit 142 is implemented, for example, by asecond registration program executed by the computer. The secondregistration unit 142 performs, when an image in a given regionincluding a position designated by the health care worker is displayedin an enlarged scale, a conversion process for the comparisondestination CT image to perform local positioning and extracts an imagein a corresponding region from the comparison destination CT image.

Consequently, the second registration unit 142 can notify the displaycontroller 143 of the image in the corresponding region. It is to benoted that, although the conversion process includes various processes,the conversion process in the present embodiment is parallel movement,and an image in a corresponding region extracted from the comparisondestination CT image by performing a conversion process is referred toas “image for which local positioning has been performed.”

Further, if an instruction for image processing is received from thehealth care worker, then the second registration unit 142 performs animage processing process for promoting an appropriate diagnosis by ahealth care worker for a tumor regarding the image in the correspondingregion.

Consequently, the second registration unit 142 can notify the displaycontroller 143 of the image in the corresponding region for which theimage processing process has been performed.

The display controller 143 is implemented, for example, by a displayprogram executed by the computer. The display controller 143 displays acomparison source CT image selected by the health care worker anddisplays a given region including a position designated by the healthcare worker in an enlarged scale on the enlarged display screen image.Further, the display controller 143 displays the image, which has beennoticed from the second registration unit 142 and for which localpositioning has been performed (in the case where an image processingprocess has been performed, the image after the image processing processis performed), in an enlarged scale on the enlarged display screenimage.

Now, a hardware configuration of the image processing apparatus 120 isdescribed. FIG. 2 is a view depicting a hardware configuration of animage processing apparatus. The image processing apparatus 120 includesa central processing unit (CPU) 201, a read only memory (ROM) 202, and arandom access memory (RAM) 203, as depicted in FIG. 2. The imageprocessing apparatus 120 further includes an auxiliary storage unit 204,a coupling unit 205, a display unit 206, an operation unit 207, and adrive unit 208. It is to be noted that the components of the imageprocessing apparatus 120 are coupled to each other by a bus 209.

The CPU 201 is a computer that executes various programs stored in theauxiliary storage unit 204 (for example, a first registration program, asecond registration program, a display program and so forth).

The ROM 202 is a nonvolatile memory. The ROM 202 functions as a mainstorage unit for storing various programs, data and so froth used forthe CPU 201 to execute the various programs stored in the auxiliarystorage unit 204. For example, the ROM 202 stores boot programs such asa basic input/output system (BIOS) or an extensible firmware interface(EFI) and so forth therein.

The RAM 203 is a volatile memory and includes a dynamic random accessmemory (DRAM), a static random access memory (SRAM) and so forth. TheRAM 203 is a main storage unit that provides a working area in whichvarious programs stored in the auxiliary storage unit 204 are developedwhen the various programs are to be executed by the CPU 201.

The auxiliary storage unit 204 is a computer-readable storage apparatusinto which various programs installed in the image processing apparatus120 and data and so forth generated by execution of the various programsare recorded.

The coupling unit 205 is coupled with the CT apparatus 110 and the imageDB 130 and transfers data to and from the CT apparatus 110 and the imageDB 130. The display unit 206 displays a CT scanned image by the CTapparatus 110 and stored in the image DB 130 through a parallel displayscreen image. The operation unit 207 accepts various operationsperformed from the image processing apparatus 120 by a health careworker.

The drive unit 208 is a device for setting a recording medium 210 settherein. The recording medium 210 here includes media in whichinformation is optically, electrically, or magnetically recorded like acompact disc (CD)-ROM, a flexible disk, or a magneto-optical disk.Further, the recording medium 210 includes semiconductor memories forelectrically recording information like a ROM, a flash memory and soforth.

It is to be noted that, in the present embodiment, the various programsstored in the auxiliary storage unit 204 are installed by loading, forexample, a distributed recording medium 210 into the drive unit 208 andreading out the various programs recorded in the recording medium 210 bythe drive unit 208 or by downloading various programs from the networkthrough the coupling unit 205.

Now, a relationship of processing contents of the diagnosis support unit140 of the image processing apparatus 120, operation contents of ahealth care worker, and a parallel display screen image displayed on thedisplay unit 206 of the image processing apparatus 120 when a process isexecuted by the diagnosis support unit 140 is described.

FIGS. 3 and 4 are views illustrating relationships among processingcontents of a diagnosis support unit of an image processing apparatus,operation contents of a health care worker, and display contents of aparallel display screen image.

In the image processing apparatus 120, if the diagnosis support unit 140is activated, then processing by the display controller 143 is started,and a parallel display screen image 300 for displaying CT scanned imagesat different times in parallel is displayed on the display unit 206 asdepicted in FIG. 3. In a state in which the parallel display screenimage 300 is displayed, a health care worker would select a scannedimage series of a given region (here, of the chest) of a given patientscanned at given time as a comparison source CT image series.Consequently, the display controller 143 reads out the selectedcomparison source CT image series from the image DB 130. Further, if agiven comparison source CT image (here, file name=“ImageA015”) isdesignated from within the selected comparison source CT image series bythe health care worker, then the display controller 143 displays thedesignated comparison source CT image in the parallel display screenimage 300.

In order to compare with the designated comparison source CT image, thehealth care worker would select a scanned image series of a same regionof the same patient scanned at a different time as a comparisondestination CT image series. For example, the health care worker wouldinput a patient identification (ID), series date and time, a body partexamined (here, the chest) and so forth for selection. Consequently, thedisplay controller 143 reads out the scanned image series specified bythe inputted patient name, series date and time, body part examined andso forth as a comparison destination CT image series from the image DB130. Further, the display controller 143 reads out a comparisondestination CT image (here, file name=“ImageB018”) corresponding to thecomparison source CT image displayed in the parallel display screenimage 300 from within the read out comparison destination CT imageseries and displays the comparison destination CT image in the paralleldisplay screen image 300.

At this time, in the diagnosis support unit 140, the first registrationunit 141 functions to perform correction using affine transformationsuch as rotation or parallel translation for the read out CT image toperform global positioning. Since global positioning is performed forthe entire CT image, global positional displacement between thecomparison source CT image and the comparison destination CT image isreduced.

After the global positioning is completed, the health care worker woulddesignate a position of a tumor F in the displayed comparison source CTimage as depicted in FIG. 4. Consequently, the display controller 143displays an image of a given region (region of interest (ROI)) 401including the position of the designated tumor F in an enlarged scale inthe enlarged displace screen image in the comparison source CT image.Further, at this time, the health care worker would additionallydesignate whether or not image processing is to be performed.

After an image of the given region (ROI) 401 is displayed in an enlargedscale, the second registration unit 142 performs local positioning for apartial region of the comparison destination CT image. Consequently, thesecond registration unit 142 extracts an image of a corresponding region402 including a position of a tumor F′ corresponding to the tumor F(image for which local positioning has been performed). It is to benoted that the second registration unit 142 performs convergencedecision (details are hereinafter described) before the localpositioning is performed.

Further, if an instruction for image processing is issued from thehealth care worker, then the second registration unit 142 performs animage processing process (details are hereinafter described) for theimage in the corresponding region 402 obtained by performing the localpositioning.

Further, the second registration unit 142 notifies the displaycontroller 143 of the image in the corresponding region 402 obtained byperforming the local positioning (in the case where an image processingprocess has been executed, the image after the image processingprocess).

The display controller 143 displays the image in the correspondingregion 402 notified from the second registration unit 142 (in the casewhere an image processing process has been executed, the image after theimage processing process) in an enlarged scale on the enlarged displayscreen image in the comparison destination CT image. Consequently, theimage for which the local positioning has been performed and besides theimage processing has been performed can be displayed.

In this manner, with the image processing apparatus 120, when theposition of the tumor F is designated on the comparison source CT imageby the health care worker, an image in the given region 401 can bedisplayed in an enlarged scale. Further, it is possible to extract animage in the corresponding region 402 from the comparison destination CTimage by performing local positioning on the basis of an image in thegiven region 401 and perform image processing for promoting appropriatediagnosis by a health care worker for a tumor and then display the imagein an enlarged scale on the enlarged display screen image.

Consequently, it becomes possible for the health care worker to easilygrasp a corresponding region between CT images included in scanned imageseries at different times and to perform appropriate diagnosis for thetumor.

Now, the image DB 130 is described. FIG. 5 is a view illustrating anexample of information stored in an image DB. As illustrated in FIG. 5,information stored in the image DB 130 is managed in a classified statefor each patient, and FIG. 5 illustrates an example of informationrelating to a patient of a patient ID=“xxx.”

As depicted in FIG. 5, items of information of a patient include “seriesdate and time,” “body part examined,” “series name,” and “image list.”In the “series date and time,” information regarding the date and timeat which the CT image was scanned is placed. In the “body partexamined,” information regarding the body part of the CT scanning targetis placed. In the “series name,” a series name for specifying a seriesconfigured from a plurality of CT scanned images is placed. In the“image list,” a plurality of CT scanned images (file names) are placed.

The example of FIG. 5 indicates that a series of the series name=“seriesA” including CT images ImageA001 to ImageA030 obtained by CT scanningfor the body part examined=“chest” at the series date and time=“Feb. 5,2014” is placed in the image DB 130. The example of FIG. 5 furtherindicates that a series of the series name=“series B” including CTimages ImageB001 to ImageB030 obtained by CT scanning for the body partexamined=“chest” at the series date and time=“Aug. 3, 2015” is placed inthe image DB 130.

It is to be noted that a broken line in FIG. 5 indicates that the CTimage of “ImageA015” is selected as a comparison source CT image by ahealth care worker. Further, it is indicated that the CT image of“ImageB018” is selected as a comparison destination CT image by a healthcare worker.

Now, individual portions of the diagnosis support unit 140 aredescribed. It is to be noted that, in the following, description isgiven principally of the second registration unit 142.

As described hereinabove, at the point of time at which the globalpositioning is completed, the comparison source CT image and thecomparison destination CT image are corrected against a generalpositional fluctuation while a local positional fluctuation remains.Therefore, when an image in the corresponding region 402 correspondingto the given region 401 including the position of the tumor F designatedby the health care worker is to be displayed in an enlarged scale, thesecond registration unit 142 first determines a local positionalfluctuation of the comparison destination CT image with respect to thecomparison source CT image. Then, the second registration unit 142performs a conversion process by parallel translation in the comparisondestination CT image in response to the determined fluctuation toperform local positioning. Consequently, the second registration unit142 can extract the image of the corresponding region 402.

Here, where the body part examined=“chest,” a fluctuation of a localposition is caused by two available principal factors (one of which isbased on the breathing or the heart beating and the other of which isbased on a change (time-dependent variation) of a tumor). FIG. 6 is aview illustrating factors of a local positional fluctuation of acomparison destination CT image with respect to a comparison source CTimage.

If a local positional fluctuation occurs as depicted in FIG. 6, then ina region of a comparison destination CT image having same coordinates asthose of a given region 401 of a comparison source CT image, forexample, an image 610 is displayed.

If an image 600 in the given region 401 of the comparison source CTimage and the image 610 in a region of the comparison destination CTimage having coordinates same as those of the given region 401 arecompared with each other in FIG. 6, then it can be recognized that theposition of a blood vessel and the position of a tumor (namely, eachposition of each part of the lung) are displaced by a great amount by alocal positional fluctuation. In FIG. 6, thick lines denote bloodvessels 601 to 603 and 611 to 613, and shadowed regions indicate tumorsF and F′.

It is to be noted that the positional fluctuation based on the breathingand the heart beating is a fluctuation of the position caused by amovement of the diaphragm, for example, upon breathing. Since theposition of the diaphragm is fluctuated depending upon whether a patientexhales or inhales, the position of portions of the lung is fluctuatedby the fluctuation of the position of the diaphragm. In particular, thecomparison source CT image and the comparison destination CT image caninclude there between a fluctuation of a local position based on thebreathing and the heart beating except when the breathing states of thepatient upon image scan coincide fully with each other.

It is to be noted that, although the positional fluctuation based on thebreathing and the heart beating is non-rigid deformation, for example,with respect to the entire body, since the given region 401 is part ofthe inside of the lung, the entire given region 401 translates inparallel in a given direction. Accordingly, the positional fluctuationcan be regarded as rigid-body motion.

On the other hand, the positional fluctuation based on a change of atumor is a fluctuation of the position of the tumor arising from that amalignant tumor such as adenocarcinoma grows while destroying thealveoli and the volume of the alveoli decreases by an amount of airhaving been held by the alveoli (in other words, involved in convergenceoccurring in the tumor).

The second registration unit 142 subtracts a fluctuation amount of theposition based on a change of the tumor (time-dependent change) frombetween the positional fluctuations based on the two factors describedabove to extract a fluctuation amount of the position based on thebreathing and the heart beating. Then, the second registration unit 142performs local positioning based on the fluctuation amount of theposition based on the breathing and the heart beating.

Here, a change (time-dependent variation) of a tumor is described inmore detail with reference to FIG. 7. FIG. 7 depict views illustrating apositional fluctuation based on a change of a tumor in more detail.

FIG. 7A illustrates a manner of surrounding tissues immediately after amalignant tumor such as adenocarcinoma is generated at a positionindicated at a tumor central point O. As illustrated in FIG. 7A, in astate immediately after the malignant tumor is generated, both of thedistance from the tumor central point O to a point D₁ of bronchus 711and the distance from the tumor central point O to a point C₁ of a bloodvessel 712 are r1.

FIG. 7B illustrates a manner in which, since the malignant tumor growswhile destroying the alveolus around the tumor, surrounding tissuesincluding the bronchus 711 and the blood vessel 712 move toward thetumor central point O. As depicted in FIG. 7B, as a result of themovement of the surrounding tissues toward the tumor central point O,both of the distance from the tumor central point O to a point D₂ of thebronchus 711 and the distance from the tumor central point O to a pointC₂ of the blood vessel 712 become r2 (<r1).

FIG. 7C illustrates a manner in which, since the malignant tumor furtherglows while destroying the alveolus around the tumor, the surroundingtissues including the bronchus 711 and the blood vessel 712 further movetoward the tumor central point O. As depicted in FIG. 7C, as a result ofthe movement of the surrounding tissues toward the tumor central pointO, both of the distance from the tumor central point O to a point D₃ ofthe bronchus 711 and the distance from the tumor central point O to apoint C₃ of the blood vessel 712 become r3 (<r2).

In this manner, a fluctuation of a position based on a change of a tumor(involved in convergence caused by the tumor) has a characteristic thatsurrounding tissues move toward the tumor central point O and can beregarded as non-rigid deformation.

It is to be noted that, as depicted in FIG. 7, tissues around the tumorcan be roughly classified into tissues of a tumor region 703, tissues ina convergence region 702, and tissues in a normal region 701. In thetumor region 703, since the tissues are destroyed by the newly appearingmalignant tumor, part of the tissues existing in FIG. 7A disappears anddoes not exist in FIG. 7C. Meanwhile, in the convergence region 702,although tissues existing in FIG. 7A exist also in FIG. 7C, it can beregarded that the position of the corresponding tissues is fluctuated ina direction toward the center (B₁→B₂→B₃). Further, in the normal region701, tissues existing in FIG. 7A still exist in FIG. 7C, and theposition (A₁→A₂→A₃) of the corresponding tissues is little fluctuated.

As apparently recognized from the description given above with referenceto FIGS. 6 and 7, the factors of a local positional fluctuation betweena comparison source CT image and a comparison destination CT imageinclude a “factor based on the breathing and the heart beating” whichcan be regarded as rigid-body motion and a “factor based on a change ofa tumor” which is a non-rigid deformation. Further, where the “factorbased on a change of a tumor,” there is a characteristic thatsurrounding tissues move toward the tumor central point O, and tissuesaround the tumor can be roughly classified into tissues in the normalregion 701, the convergence region 702, and the tumor region 703 inaccordance with the degree.

Now, problems when the second registration unit 142 performs localpositioning for a region in which such a rigid-body motion and non-rigiddeformation as depicted in FIG. 6 exist in a mixed state in a comparisondestination CT image are described with reference to FIGS. 8 and 9.

As described hereinabove, when local positioning is to be performed in acomparison destination CT image, the second registration unit 142performs a conversion process by parallel translation. In other words,the second registration unit 142 performs not a conversion processassuming a non-rigid body but a conversion process assuming a rigidbody.

Here, when to perform a conversion process by parallel translation, thesecond registration unit 142 performs calculation of a representativevector indicative of to which position of the comparison destination CTimage the given region 401 moves (positional relationship between thegiven region 401 and the corresponding region 402).

FIG. 8 depict views illustrating a calculation process of arepresentative vector and a calculation process of a correspondingregion. In particular, FIG. 8A illustrates a corresponding vector (darkarrow mark) that is the difference between the position of a featurepoint included in the given region 401 of the comparison source CT imageand the position of a feature point in the comparison destination CTimage corresponding to the feature point. It is to be noted that aregion 800 is a region in which a feature point in the comparisondestination CT image corresponding to the feature point included in thegiven region 401 of the comparison source CT image is included, and is aregion used for calculation of a representative vector. In thefollowing, the region in the comparison destination CT image is referredto as representative vector calculation target region 800.

Here, it is assumed that the second registration unit 142 calculates arepresentative vector 810 using all corresponding vectors included inthe representative vector calculation target region 800. In this case,an image for which local positioning has been performed can be extractedby executing a process illustrated in FIG. 8B.

FIG. 8B illustrates a manner in which an image for which localpositioning has been formed from a comparison destination CT image isextracted by performing a conversion process by parallel translationusing the representative vector 810. As depicted in FIG. 8B, the secondregistration unit 142 determines a region 802 by parallelly translatinga region 801 in the comparison destination CT image having coordinatessame as those of the given region 401 of the comparison source CT imageon the basis of the representative vector 810. Then, the secondregistration unit 142 extracts an image of the region 802 from thecomparison destination CT image to extract an image for which localpositioning has been performed.

However, the image extracted in this manner is nothing but an imageobtained by determining a representative vector assuming that only arigid-body motion occurs in a region in which a rigid body motion andnon-rigid deformation exist in a mixed manner and then parallellytranslating the image so as to cancel the assumed rigid-body motion. Inshort, the parallel translation is performed so as to cancel also theinfluence of the non-rigid deformation amount.

More detailed description is given with reference to FIGS. 8C and 8D.FIG. 8C illustrates corresponding vectors of fluctuation amounts(rigid-body motion amounts) of positions based on the breathing and theheart beating from among corresponding vectors each of which links theposition of a feature point included in the given region 401 of thecomparison source CT image and the position of a feature point in thecomparison destination CT image corresponding to the feature point. Asdepicted in FIG. 8C, the corresponding vectors of the rigid body motionamounts are directed in the same direction and have an equal length. Itis to be noted that the corresponding vectors of the rigid motionamounts exist in the normal region 701 and the convergence region 702.However, since a feature point of the comparison destination CT imagecorresponding to a feature point of the comparison source CT image doesnot exist in the tumor region 703, no corresponding vector exists in thetumor region 703.

Meanwhile, FIG. 8D illustrates corresponding vectors of fluctuationamounts (non-rigid deformation amounts) of positions based on a changeof the tumor from among corresponding vectors that link the positions ofthe feature points included in the given region 401 of the comparisonsource CT image and the positions of feature points in the comparisondestination CT image corresponding to the feature points. As depicted inFIG. 8D, a corresponding vector of a non-rigid deformation amount existsonly in the convergence region 702 (in this regard, except the tumorregion 703) and can be regarded as being directed toward the center.

In this manner, the corresponding vectors corresponding to the rigidbody motions and the corresponding vectors corresponding to thenon-rigid deformations are different in length and direction of a vectorfrom each other and are different also in existing position.

On the other hand, the corresponding vectors illustrated in FIG. 8A aresums of the corresponding vectors depicted in FIG. 8C and thecorresponding vectors depicted in FIG. 8D.

In particular, the corresponding vectors existing at a positioncorresponding to the convergence region 702 from among the correspondingvectors depicted in FIG. 8A include the corresponding vectorscorresponding to rigid-body motions and the corresponding vectorscorresponding to the non-rigid deformations in a mixed state. Therefore,if the representative vector 810 is calculated from correspondingvectors including the corresponding vectors existing at the positioncorresponding to the convergence region 702, then the representativevector 810 includes an influence of non-rigid deformation. Even if sucha representative vector 810 as just described is used to perform localpositioning, positioning of high accuracy is not expectable.

Description is given using a particular image. FIG. 9 is a viewdepicting an image for which local positioning has been performed usinga representative vector including an influence of non-rigid deformation.It is to be noted that, in the example of FIG. 9, an image 900 for whichlocal positioning has been performed (image of the region 802 of thecomparison destination CT image) and the image 600 of the given region401 of the comparison source CT image are depicted in an overlappingrelationship with each other.

As depicted in FIG. 9, the positions of blood vessels 901 to 903 and atumor F′ included in the image 900 are displaced from the positions ofthe blood vessels 601 to 603 and the tumor F included in the image 600although local positioning has been performed.

Taking the problem in calculation of a representative vector in such aregion in which rigid-body motions and non-rigid deformations exist in amixed manner as described above into consideration, the secondregistration unit 142 in the present embodiment determines arepresentative vector removing an influence of non-rigid deformation andperforms local positioning. Further, the second registration unit 142 inthe present embodiment performs an image processing process for an imageof the corresponding region 402 obtained by performing the localpositioning to visualize and display an influence of the non-rigiddeformation (namely, a fluctuation amount of a position based on achange of the tumor (time-dependent variation)).

In the following, a functional configuration of the second registrationunit 142 in the present embodiment is described with reference to FIG.10, and functions of components of the second registration unit 142 aredescribed with reference to FIGS. 11 to 15. Further, details ofprocesses executed by the second registration unit 142 are describedwith reference to flowcharts of FIGS. 16 to 19.

FIG. 10 is a view depicting a functional configuration of a secondregistration unit. As depicted in FIG. 10, the second registration unit142 includes a region identification unit 1001, a corresponding vectorcalculation unit 1002, a convergence region decision unit 1003, arepresentative vector calculation unit 1004, a positioning unit 1005,and an image processor 1006.

The region identification unit 1001 identifies a given region 401including a position designated by a health care worker. For example,the region identification unit 1001 acquires coordinates on a comparisonsource CT image which specify the position of the given region 401.

The corresponding vector calculation unit 1002 extracts feature pointsfrom the given region 401 of the comparison source CT image identifiedby the region identification unit 1001. Further, the correspondingvector calculation unit 1002 searches for feature points in a comparisondestination CT image corresponding to the extracted feature points.Further, the corresponding vector calculation unit 1002 calculatescorresponding vectors on the basis of a difference of the positions ofthe feature points extracted from the comparison source CT image and thepositions of the feature points in the comparison destination CT imagecorresponding to the feature points.

The convergence region decision unit 1003 decides whether or not aconvergence region 702 is included in the representative vectorcalculation target region 800 on the basis of the corresponding vectorscalculated by the corresponding vector calculation unit 1002. Further,if it is decided that the convergence region 702 is included, then theconvergence region decision unit 1003 calculates a boundary positionbetween the normal region 701 and the convergence region 702. Further,the convergence region decision unit 1003 notifies the representativevector calculation unit 1004 of a result of the decision of whether ornot a convergence region 702 is included and a result of the calculationof the boundary position between the normal region 701 and theconvergence region 702.

The representative vector calculation unit 1004 calculates arepresentative vector in the representative vector calculation targetregion 800 on the basis of the corresponding vectors calculated by thecorresponding vector calculation unit 1002. If it is decided that aconvergence region 702 is not included in the representative vectorcalculation target region 800, then the representative vectorcalculation unit 1004 calculates a representative vector using allcorresponding vectors in the representative vector calculation targetregion 800 (in this regard, except the tumor region). On the other hand,if it is decided that a convergence region 702 is included in therepresentative vector calculation target region 800, then therepresentative vector calculation unit 1004 calculates a representativevector using the corresponding vectors in the representative vectorcalculation target region 800 except the corresponding vectors includedin the convergence region (and the tumor region).

It is to be noted that, in the present embodiment, the representativevector calculation unit 1004 performs an averaging process when it is tocalculate a representative vector using the corresponding vectors.

The positioning unit 1005 extracts an image of a corresponding region402 corresponding to the given region 401 from the comparisondestination CT image on the basis of the representative vectorcalculated by the representative vector calculation unit 1004. Forexample, the positioning unit 1005 moves the coordinates that specifythe position of the given region 401 using the representative vector onthe comparison destination CT image to calculate the coordinates afterthe movement. Further, the positioning unit 1005 acquires an image forwhich local positioning has been performed by extracting the image ofthe region (corresponding region 402) specified by the calculatedcoordinates after the movement from the comparison destination CT image.

The image processor 1006 performs image processing of the image of thecorresponding region 402 obtained by performing the local positioningfor visualizing the fluctuation amount of the position based on a changeof the tumor (time-dependent variation). If an instruction for imageprocessing from a health care worker is issued, then the image processor1006 performs image processing for the image of the corresponding region402.

For example, the image processor 1006 visualizes a change of a tissuebased on a change of the tumor by calculating a difference between pixelvalues between the image of the given region 401 and the image of thecorresponding region 402. Further, the image processor 1006 visualizes amoving direction and a movement amount of the tissue based on the changeof the tumor by calculating a vector indicative of a fluctuation of theposition based on the change of the tumor between the image of the givenregion 401 and the image of the corresponding region 402. Further, theimage processor 1006 visualizes a tendency of the change of the tissuebased on the change of the tumor on the basis of a distribution of thecalculated vectors in the corresponding region 402.

It is to be noted that the image processor 1006 notifies the displaycontroller 143 of the image of the corresponding region 402 for whichthe image processing has been performed. Consequently, the displaycontroller 143 can cause an image obtained by performing the imageprocessing for the image of the corresponding region obtained by thelocal positioning to be displayed in an enlarged scale on the enlargeddisplay screen image.

Now, a particular example of the functions of the convergence regiondecision unit 1003, the representative vector calculation unit 1004, thepositioning unit 1005, and the image processor 1006 from among thecomponents included in the second registration unit 142 depicted in FIG.10 is described.

First, a particular example of functions of the convergence regiondecision unit 1003 is described. FIG. 11 is a view illustratingprocessing contents of a convergence region decision unit.

FIG. 11 illustrates a manner in which the representative vectorcalculation target region 800 is partitioned by delimiting lines of agiven step size into rectangular frame shapes from the center to sideedges of the representative vector calculation target region 800 and itis decided on the basis of corresponding vectors of the partitionswhether or not a convergence region 702 is included in therepresentative vector calculation target region 800.

It is to be noted that the distance from the center to a side edge ofthe representative vector calculation target region 800 is representedby R, and the step size is represented by ΔR. Further, while a case inwhich the representative vector calculation target region 800 ispartitioned into rectangular frame shapes is described, therepresentative vector calculation target region 800 may be partitionedinto ring shapes instead of rectangular frame shapes.

The convergence region decision unit 1003 extracts corresponding vectorsincluded in a partition group 1101 within the range of R to (R−ΔR)(shadowed region in the representative vector calculation target region800 depicted on the left side in FIG. 11). Further, the convergenceregion decision unit 1003 extracts corresponding vectors included in apartition group 1102 within the range of (R−ΔR) to (R−ΔR×2) (shadowedregion in the representative vector calculation target region 800depicted at the center in FIG. 11).

Further, the convergence region decision unit 1003 calculates adifference between each corresponding vectors adjacent each otherbetween the corresponding vectors of the partition group 1101 and thecorresponding vectors of the partition group 1102 from among theextracted corresponding vectors to determine difference vectors. It canbe considered that the difference vector here indicates a difference inchange of the position of a feature point between the comparison sourceCT image and the comparison destination CT image. The vectors in therepresentative vector calculation target region 800 depicted on theright side in FIG. 11 indicate an example of difference vectorscalculated on the basis of the corresponding vectors of the partitiongroup 1101 and the corresponding vectors of the partition group 1102.

If each difference vector determined in this manner is greater than agiven threshold value, then the convergence region decision unit 1003decides a direction of the difference vector. Further, if the directionof the difference vector can be regarded as being directed toward thecenter of the representative vector calculation target region 800 (whichrepresents a collapsing change), then the convergence region decisionunit 1003 decides that a convergence region is included (namely, theconvergence region decision unit 1003 detects a convergence region).Further, the convergence region decision unit 1003 decides a boundaryposition between two partition groups in which the corresponding vectorsfor calculating the difference vectors used for the decision that aconvergence region is included exist as the boundary position betweenthe normal region and the convergence region.

It is to be noted that, as apparent from the description given abovewith reference to FIG. 11, the convergence region decision unit 1003first determines a difference vector using corresponding vectorsextracted from the partition group 1101 positioned at the outermost sideof the representative vector calculation target region 800. This isbecause the corresponding vectors can be estimated as correspondingvectors involved in a fluctuation of the position based on the breathingand the heart beating, which are not influenced by a fluctuation of theposition based on a change of the tumor.

Further, the convergence region decision unit 1003 calculates adifference between adjacent corresponding vectors. This is because thereis no great difference in fluctuation of the position based on thebreathing and the heart beating between adjacent corresponding vectorsand, by calculating the difference, an influence of the fluctuation ofthe position based on the breathing and the heart beating can becancelled. In other words, it can be considered that a difference vectordetermined by calculating the difference between adjacent correspondingvectors (it is to be noted that the difference vector has a magnitudeequal to or greater than a given threshold value) represents acorresponding vector corresponding to a fluctuation amount of theposition based on a change of the tumor.

It is to be noted that the reason why the convergence region decisionunit 1003 decides a direction of a difference vector is that, because acorresponding vector in a convergence region has a property that it isdirected toward the tumor central point O, this is effective to identify(or detect) that the corresponding vector is a fluctuation of theposition based on the change of the tumor.

Now, a particular example of the function of the representative vectorcalculation unit 1004 is described. FIG. 12 depict views illustrating acalculation method of a representative vector when it is decided that aconvergence region exists.

If the representative vector calculation target region 800 includes aconvergence region 702, then the representative vector calculation unit1004 determines a representative vector from among corresponding vectorscalculated by the representative vector calculation target region 800except the corresponding vectors existing in the convergence region 702.In the example of FIG. 12A, 15 corresponding vectors (indicated by darkarrow marks) are calculated by the representative vector calculationtarget region 800, and the representative vector calculation unit 1004calculates a representative vector using 11 corresponding vectorsexcluding the four corresponding vectors existing in the convergenceregion 702 from among the 15 corresponding vectors.

A representative vector 1200 indicates a representative vectorcalculated using the 11 corresponding vectors. In this manner, byexcluding the four corresponding vectors existing in the convergenceregion 702, a representative vector can be determined excluding theinfluence of non-rigid deformation (namely, a fluctuation amount of theposition based on the change of the tumor (time-dependent change)).

FIG. 12B illustrates a manner in which an image for which localpositioning has been performed is extracted from a comparisondestination CT image by performing a conversion process by paralleltranslation using the representative vector 1200. As depicted in FIG.12B, the second registration unit 142 can determine the correspondingregion 402 by parallelly translating the region 801 in the comparisondestination CT corresponding to the given region 401 of the comparisonsource CT image in response to the representative vector 1200. Further,by extracting an image of the corresponding region 402 from thecomparison destination CT image, the second registration unit 142 canextract an image for which local positioning has been performed.

Meanwhile, FIG. 13 depict views illustrating a calculation method of arepresentative vector when it is decided that a convergence region doesnot exist. If the convergence region 702 is not included in therepresentative vector calculation target region 800, then therepresentative vector calculation unit 1004 determines a representativevector using the corresponding vectors calculated by the representativevector calculation target region 800. However, the corresponding vectorsincluded in the tumor region 703 are excluded. It is to be noted that,since a corresponding point to a feature point does not exist in thetumor region 703, no corresponding vector exists, and therefore, thecalculated representative vector is same irrespective of whether or notcorresponding vectors existing in the tumor region 703 are excluded.

In the example of FIG. 13A, 15 corresponding vectors (indicated by darkarrow marks) are calculated in the representative vector calculationtarget region 800, and the representative vector calculation unit 1004calculates a representative vector using the 15 corresponding vectors. Arepresentative vector 1300 indicates a representative vector calculatedusing the 15 corresponding vectors. In this manner, when the convergenceregion 702 is not included in the representative vector calculationtarget region 800, since there is no influence of non-rigid deformation,a representative vector can be calculated using all correspondingvectors.

FIG. 13B illustrates a manner in which an image for which localpositioning has been performed is extracted from the comparisondestination CT image by performing a conversion process by paralleltranslation using the representative vector 1300. As illustrated in FIG.13B, the second registration unit 142 can determine the correspondingregion 402 by parallelly translating the region 801 in the comparisondestination CT image corresponding to the given region 401 of thecomparison source CT image in response to the representative vector1300. Further, the second registration unit 142 can extract an image forwhich local positioning has been performed by extracting an image of thecorresponding region 402 from the comparison destination CT image.

Here, an image obtained by the positioning unit 1005 performing localpositioning using the representative vector 1200 that is free from aninfluence of non-rigid deformation is described. FIG. 14 is a viewdepicting an image obtained by performing local positioning using arepresentative vector from which an influence of non-rigid deformationhas been removed.

It is to be noted that, in the example of FIG. 14, an image 1400 of thecorresponding region 402 obtained by the positioning unit 1005performing local positioning using the representative vector 1200 fromwhich the influence of non-rigid deformation has been excluded and theimage 600 of the given region 401 of the comparison source CT image aredepicted in an overlapping relationship with each other.

As depicted in FIG. 14, the positions of blood vessels 1401 to 1403 andthe tumor F′ included in the image 1400 are substantially same as theblood vessels 601 to 603 and the tumor F included in the image 600,respectively. In other words, in the image 1400, the fluctuation of thepositions based on the breathing and the heart beating is cancelled.Meanwhile, the blood vessels 1402 and 1403 which are positioned aroundthe tumor F′ are displaced in position from the blood vessels 601 to 603included in the image 600 which are positioned around the tumor F. Inother words, in the image 1400, the influence of the fluctuation of thepositions based on the tumor remains.

Now, a particular example of the function of the image processor 1006that performs an image processing process for an image of thecorresponding region 402 obtained by the positioning unit 1005performing local positioning is described. FIG. 15 depict viewsillustrating a particular example of an image processing processexecuted by an image processor. FIG. 15A illustrates a manner in which achange of a tissue based on a change of a tumor is visualized by theimage processor 1006 calculating a difference between pixel values of animage 600 of a given region 401 and an image 1400 of a correspondingregion 402.

As depicted in FIG. 15A, the tumor included in the image 1400 of thecorresponding region 402 is greater than the tumor included in the image600 of the given region 401, and a cavity is generated in the inside ofthe tumor because of disappearance of a tissue.

The image processor 1006 calculates differences between pixel values ofpixels of the image 600 of the given region 401 and pixel values ofpixels of the image 1400 of the corresponding region 402 to generate adifference image 1510 thereby to visualize a variation between the image600 and the image 1400.

For example, the image processor 1006 decides pixels whose differencevalue is in the positive and is greater than a given threshold valuefrom among the pixels of the difference image 1510 as pixels indicativeof a tumor having appeared newly and colors the pixels, for example, inred. Further, the image processor 1006 determines pixels whosedifference value is in the negative and is smaller than a giventhreshold value from among the pixels of the difference image 1510 aspixels indicative of a disappeared tissue and colors the pixels, forexample, in blue. It is to be noted that, from among the pixels of thedifference image 1510, those pixels the absolute value of the differencevalue of which is equal to or lower than the given threshold value aredecided as pixels having no change, and change in color is not performedfor the pixels.

Consequently, if a health care worker observes the difference image1510, then the health care worker can recognize a change of a tissuebased on a change of the tumor (appearance, disappearance, or no change)readily.

FIGS. 15B-1 and 15B-2 illustrate a manner in which a moving directionand a movement amount of a tissue based on a change of the tumor arevisualized by the image processor 1006 displaying vectors indicative offluctuations of the position based on a change of the tumor.

Particularly, FIG. 15B-1 illustrates a manner in which the imageprocessor 1006 acquires difference vectors calculated by the convergenceregion decision unit 1003 (for example, difference vectors illustratedin FIG. 11) and displays the difference vectors on a translucent layersuperposed on the image 1400. By displaying the difference vectors in anoverlapping relationship with the image 1400 through the translucentlayer in this manner, the health care worker can easily recognize themoving direction and the movement amount of the tumor based on thechange of the tumor.

Meanwhile, FIG. 15B-2 illustrates a manner in which the image processor1006 acquires difference vectors calculated by the convergence regiondecision unit 1003 and superposes a translucent layer, which is coloredat corresponding positions of the partition group 1102, on the image1400. It is to be noted that the image processor 1006 colors thetranslucent layer at positions of the partition group 1102 correspondingto the difference vectors with shading corresponding to the magnitudesof the difference vectors. For example, as the magnitude of thedifference vector increases, the partition at the corresponding positionis colored in a more saturated color, and as the magnitude of thedifference vector decreases, the partition at the corresponding positionis colored in a less saturated color.

Consequently, the health care worker can recognize that, at the positionof a colored partition, movement of a tissue has occurred on the basisof a change of a tumor. Further, the health care worker can recognizethe magnitude of the movement amount of the tissue based on the changeof the tumor on the basis of the saturation of the color.

FIGS. 15C-1 and 15C-2 illustrate a manner in which a tendency of achange of a tissue based on a change of a tumor is visualized by theimage processor 1006 on the basis of a distribution of differencevectors acquired from the convergence region decision unit 1003. It isillustrated in a manner in which the image processor 1006 calculates thenumber per unit area of difference vectors acquired from the convergenceregion decision unit 1003 and superposes a translucent layer colored ina region corresponding to a convergence region with saturation inaccordance with the calculated number of difference vectors on an image1400.

Consequently, the health care worker can recognize a tendency of achange of the tissue based on a change of the tumor (whether the numberof tissues whose positions have been changed by a great amount is greator small) on the basis of the saturation of the applied color. FIG.15C-1 illustrates a manner in which a region corresponding to theconvergence region 702 is colored in a more saturated color because thenumber of difference vectors per unit area is great in the regioncompared with FIG. 15C-2.

With the displays depicted in FIGS. 15C-1 and 15C-2, it can berecognized readily at which location difference vectors are concentratedalthough, if difference vectors are merely displayed in such a case thata plurality of regions that may possibly suffer from adenocarcinomaexist in the proximity of each other, then it is less likely torecognize at which location the difference vectors are concentrated.

Now, a flow of processing executed by the second registration unit 142is described. FIG. 16 is a first flow chart of processing executed by asecond registration unit.

At step S1601, the region identification unit 1001 identifies a givenregion (ROI) 401 centered at the position of a tumor F designated by ahealth care worker on a comparison source CT image.

At step S1602, the corresponding vector calculation unit 1002 extractsfeature points from the given region 401 of the comparison source CTimage identified by the region identification unit 1001. Further, thecorresponding vector calculation unit 1002 searches for feature pointsin a comparison destination CT image corresponding to the extractedfeature points.

At step S1603, the convergence region decision unit 1003 extracts aregion including the feature points searched out from the comparisondestination CT image as a representative vector calculation targetregion 800.

At step S1604, the corresponding vector calculation unit 1002 calculatescorresponding vectors on the basis of the differences between thepositions of the feature points extracted from the comparison source CTimage and the positions of the feature points in the comparisondestination CT image corresponding to the feature points.

At step S1605, the convergence region decision unit 1003 decides on thebasis of calculated corresponding vectors whether or not a convergenceregion 702 is included in the representative vector calculation targetregion 800. If it is decided that a convergence region 702 is included,then the convergence region decision unit 1003 calculates a boundaryposition between the normal region 701 and the convergence region 702.It is to be noted that a detailed flow chart of the convergence regiondecision process at step S1605 is hereinafter described.

At step S1606, the representative vector calculation unit 1004 decideswhether or not a convergence region 702 is included on the basis of aresult of the convergence region decision process (step S1605). If it isdecided at step S1606 that a convergence region 702 is not included,then the processing advances to step S1607. At step S1607, therepresentative vector calculation unit 1004 and the positioning unit1005 perform a local positioning process for a tumor other thanadenocarcinoma.

On the other hand, if it is decided at step S1606 that a convergenceregion 702 is included, then the processing advances to step S1608. Atstep S1608, the representative vector calculation unit 1004 and thepositioning unit 1005 perform a local positioning process foradenocarcinoma.

It is to be noted that a detailed flow chart of the local positioningprocess at steps S1607 and S1608 is hereinafter described.

At step S1609, the image processor 1006 decides whether or not aninstruction to perform image processing has been accepted from a healthcare worker. If an instruction to perform image processing has not beenaccepted, then the processing advances to step S1611. In this case, atstep S1611, an image of the corresponding region 402 obtained by thelocal positioning process performed at step S1607 or step S1608 isoutputted to the display controller 143. As a result, the displaycontroller 143 causes an image of the corresponding region 402 obtainedby the local positioning process performed at step S1607 or step S1608(image for which image processing has not been performed) to bedisplayed in an enlarged scale on an enlarged display screen image.

On the other hand, if it is decided at step S1609 that an instruction toperform image processing has been accepted, then the processing advancesto step S1610. At step S1610, the image processor 1006 performs an imageprocessing process based on a time-dependent variation. Thereafter, theprocessing advances to step S1611.

In this case, at step S1611, an image (including a translucent layer)obtained by performing the image processing process (step S1610) for theimage of the corresponding region 402 obtained by performing the localpositioning process (step S1608) is outputted to the display controller143. As a result, the display controller 143 causes the image (includingthe translucent layer) obtained by performing the image processingprocess for the image obtained by the local positioning processperformed at step S1608 to be displayed in an enlarged scale on theenlarged display screen image. It is to be noted that a detailed flowchart of the image processing process based on a time-dependentvariation at step S1610 is hereinafter described.

Now, details of the convergence region decision process (step S1605) aredescribed. FIG. 17 is a flow chart of a convergence region decisionprocess.

At step S1701, the convergence region decision unit 1003 partitions therepresentative vector calculation target region 800 from the center(tumor central point O) to the side edges into ring shapes orrectangular frame shapes with a step size ΔR. At step S1702, theconvergence region decision unit 1003 substitutes 1 into a counter i.

At step S1703, the convergence region decision unit 1003 extracts apartition group in a range of (R−ΔR×(i−1)) to (R−ΔR×i) and a partitiongroup within a range of (R−ΔR×i) to (R−ΔR×(i+1)) positioned at the innerside (side nearer to the tumor) of the partition group.

At step S1704, the convergence region decision unit 1003 calculates adifference between each adjacent ones of the corresponding vectorsexisting in the extracted partition groups to determine differencevectors.

At step S1705, the convergence region decision unit 1003 decides whetheror not the magnitude of each difference vector is within a thresholdvalue. If it is decided at step S1705 that the magnitude of thedifference vector is within the threshold value, then the processingadvances to step S1706, at which the counter i is incremented.

At step S1707, the convergence region decision unit 1003 decides whetheror not i≥R/ΔR is satisfied. If it is decided that the inequality is notsatisfied, then the convergence region decision unit 1003 decides that apartition group exists at the further inner side (side nearer to thetumor). Then, the processing returns to step S1703.

On the other hand, if it is decided at step S1707 that i≥R/ΔR issatisfied, then the convergence region decision unit 1003 decides that adifference vector has been calculated with regard to all partitiongroups. Then, the processing advances to step S1708.

At step S1708, the convergence region decision unit 1003 decides that aconvergence region 702 is not included in the representative vectorcalculation target region 800 and ends the convergence region decisionprocess.

On the other hand, if it is decided at step S1705 that the magnitude ofthe difference vector is greater than the threshold value, then theprocessing advances to step S1709. At step S1709, the convergence regiondecision unit 1003 decides whether or not it can be regarded that thedirection of the difference vector is directed toward the center of therepresentative vector calculation target region 800.

If it is decided at step S1709 that it is difficult to regard that thedirection of the difference vector is directed toward the center, thenthe processing advances to step S1706. On the other hand, if it isdecided at step S1709 that it can be regarded that the direction of thedifference vector is directed toward the center, then the processingadvances to step S1710.

At step S1710, the convergence region decision unit 1003 decides that aconvergence region 702 is included in the representative vectorcalculation target region 800, and the processing advances to stepS1711. At step S1711, the convergence region decision unit 1003 decidesthat the position at which the distance from the center of therepresentative vector calculation target region 800 is equal to (R−ΔR×i)is a boundary position between the normal region 701 and the convergenceregion 702, thereby ending the convergence region decision process.

Now, details of the local positioning process (steps S1607 and S1608)are described. FIG. 18 depict flow charts of a local positioningprocess.

In particular, FIG. 18A is a flow chart of the local positioning process(for other than adenocarcinoma). At step S1801, the representativevector calculation unit 1004 masks the tumor region 703 from within therepresentative vector calculation target region 800.

At step S1802, the representative vector calculation unit 1004calculates a representative vector using the corresponding vectors inthe region other than the tumor region 703 masked at step S1801 fromamong the corresponding vectors included in the representative vectorcalculation target region 800.

At step S1803, the positioning unit 1005 extracts an image of thecorresponding region 402 corresponding to the given region 401 from thecomparison destination CT image using the calculated representativevector. Consequently, an image for which local positing has beenperformed can be extracted.

Meanwhile, FIG. 18B is a flow chart of local positioning (foradenocarcinoma). At step S1811, the representative vector calculationunit 1004 masks the convergence region 702 including the tumor region703 from within the representative vector calculation target region 800.

At step S1812, the representative vector calculation unit 1004calculates a representative vector using the corresponding vectors inthe region other than the convergence region 702 masked at step S1811from among the corresponding vectors included in the representativevector calculation target region 800.

At step S1813, the positioning unit 1005 extracts the image 1400 of thecorresponding region 402 corresponding to the given region 401 from thecomparison destination CT image using the calculated representativevector. Consequently, an image for which local positioning has beenperformed can be extracted.

In this manner, if the second registration unit 142 in the presentembodiment decides that the magnitude of a difference vector is withinthe threshold value and the region in question is a normal region 701,then the second registration unit 142 calculates a representative vectorusing the corresponding vectors of the partitions. Further, if it isdecided that the convergence region 702 is included according to thedifference vectors have a magnitude greater than the threshold value andbeside are directed toward the center, the second registration unit 142calculates a representative vector using corresponding vectors in whichthe partition is far from the tumor compared with the partition of theconvergence region 702. In other words, the second registration unit 142calculates a representative vector using corresponding vectors of thefeature points in positions spaced by equal to or more than thethreshold value from the tumor region. Consequently, since arepresentative vector can be calculated excluding the influence ofnon-rigid deformation, the accuracy in local positioning can be raised.

Now, a flow of processing when the image processing process (step S1610)based on a time-dependent variation is performed for the image 1400 ofthe corresponding region 402 obtained by the local positioning performedas described above is described in detail.

FIG. 19 is a flow chart of an image processing process based on atime-dependent variation. At step S1901, the image processor 1006decides whether or not image processing for visualizing a change of atissue is to be performed. On a parallel display screen image 300, ahealth care worker can perform an operation (first operation) forinstructing to perform image processing for visualizing a change of atissue through the operation unit 207. Therefore, the image processor1006 decides whether or not a first operation has been performed by ahealth care worker to determine whether or not image processing forvisualizing a change of a tissue is to be performed.

If it is decided that a first operation has not been performed by ahealth care worker, then the image processor 1006 advances theprocessing to step S1905. On the other hand, if it is decided that afirst operation has been performed by a health care worker, then theimage processor 1006 advances the processing to step S1902.

At step S1902, the image processor 1006 calculates a difference valuebetween each pixel value of the image 600 of the given region 401 of thecomparison source CT image and a corresponding pixel value of the image1400 of the corresponding region 402 of the comparison destination CTimage to generate a difference image 1510.

At step S1903, the image processor 1006 classifies the difference image1510 into an appearance region, a disappearance region, or a no-changeregion on the basis of the difference values of the pixels included inthe generated difference image 1510.

At step S1904, the image processor 1006 performs color conversion forthe image 1400 of the corresponding region 402 of the comparisondestination CT image on the basis of a result of the classification atstep S1903. For example, the image processor 1006 converts pixelscorresponding to an appearance region of the image 1400 of thecorresponding region 402 of the comparison destination CT image intothose of a given color (for example, red). Further, the image processor1006 converts pixels corresponding to a disappearance region of theimage 1400 of the corresponding region 402 of the comparison destinationCT image into those of another given color (for example, blue).

At step S1905, the image processor 1006 determines whether or not imageprocessing for visualizing a moving direction and a movement amount of atissue is to be performed. A health care worker can perform, on theparallel display screen image 300, an operation (second operation) forinstructing to perform image processing for visualizing the movingdirection and the movement amount of a tissue through the operation unit207. Therefore, the image processor 1006 decides whether or not a secondoperation has been performed by a health care worker to determinewhether or not image processing for visualizing the moving direction andthe movement amount of a tissue is to be performed.

If it is decided that a second operation has not been performed by ahealth care worker, then the image processor 1006 advances theprocessing to step S1907. On the other hand, if it is decided that asecond operation has been performed by a health care worker, then theimage processor 1006 advances the processing to step S1906.

At step S1906, the image processor 1006 acquires difference vectorscalculated by the convergence region decision unit 1003. Further, theimage processor 1006 causes an image of the acquired difference vectorsto be displayed on a translucent layer to be superposed on the image1400 of the corresponding region 402. Further, the image processor 1006superposes a translucent layer, on which the difference vectors aredisplayed, on the image 1400 of the corresponding region 402.

At step S1907, the image processor 1006 determines whether or not imageprocessing for visualizing a tendency of a change of a tissue is to beperformed. A health care worker can perform an operation (thirdoperation) for instructing to perform image processing for visualizing atendency of a change of a tissue on the parallel display screen image300 through the operation unit 207. Therefore, the image processor 1006decides whether or not a third operation has been performed by a healthcare worker to determine whether or not image processing for visualizinga tendency of a change of a tissue is to be performed.

If it is decided that a third operation has not been performed by ahealth care worker, then the image processor 1006 ends the imageprocessing process based on a time-dependent variation. On the otherhand, if it is decided that a third operation has been performed by ahealth care worker, then the image processor 1006 advances theprocessing to step S1908.

At step S1908, the image processor 1006 acquires the difference vectorscalculated by the convergence region decision unit 1003. Further, theimage processor 1006 calculates the number of acquired differencevectors per unit area and determines saturation in accordance with thecalculated number.

At step S1909, the image processor 1006 colors a region corresponding tothe convergence region on the translucent layer to be superposed on theimage 1400 of the corresponding region 402 with a given color based onthe determined saturation. Further, the image processor 1006 superposesa translucent layer colored in the region thereof corresponding to theconvergence region. Further, the image processor 1006 superposes thetranslucent layer colored in the region thereof corresponding to theconvergence region on the image 1400 of the corresponding region 402.

As apparent from the description given above, in the present embodiment,if an image of a given region including a tumor in a comparison sourceCT image is designated, then a conversion process by paralleltranslation is performed in the comparison destination CT image usingcorresponding vectors spaced from the convergence region to extract animage of the corresponding region. Consequently, local positioning ofhigh accuracy from which an influence of non-rigid deformation (afluctuation amount of the position based on a change of the tumor(time-dependent variation)) is removed can be performed.

Further, in the present embodiment, image processing for visualizing afluctuation amount of the position based on a time-dependent variationof a tumor is performed for an image of a corresponding region obtainedby performing local positioning.

Consequently, even in a case in which an image is deformed by aninfluence of the breathing or the heart beating, a health care workercan easily decide a location converged by an influence of the alveolicollapsed by the tumor. As a result, the load when a health care workermakes a decision from the comparison destination CT image can bereduced.

Second Embodiment

The second registration unit in the first embodiment described abovedetermines a given region 401 in response to designation of the positionof a tumor F on a comparison source CT image by a health care worker. Incontrast, a second registration unit in the second embodiment scans acomparison source CT image successively for regions of a given size asROI candidates with a given scanning width and executes a convergenceregion decision process at each of the scanning positions. Further, thesecond registration unit in the second embodiment determines an ROIcandidate at a position at which it is decided that a convergence regionexists as a given region (ROI) 401. In this manner, in the secondembodiment, since a given region (ROI) 401 is determined on the basis ofa result of decision regarding whether or not there exists a convergenceregion, a health care worker may not perform a process for designating atumor F on a comparison source CT image. As a result, the load on thehealth care worker upon diagnosis can be reduced. In the following, thesecond embodiment is described principally in connection withdifferences thereof from the first embodiment described hereinabove.

FIG. 20 is a view illustrating a relationship between processingcontents of a diagnosis supporting unit, operation contents of a healthcare worker, and display contents of a parallel display screen image inan image processing apparatus. FIG. 20 corresponds to FIG. 4 (viewdepicting processing contents and so forth after global positioning)depicted in connection with the first embodiment.

As depicted in FIG. 20, when global positioning is completed, the secondregistration unit 142 accepts an instruction regarding whether or notimage processing is to be performed from a health care worker. Further,the second registration unit 142 scans the comparison source CT imagefor ROI candidates 2001 of a given size with a given scanning width andexecutes a convergence region decision process (step S1605) at each ofthe scanning positions. Then, the second registration unit 142determines a region to be specified by the ROI candidate 2001 at aposition at which it has been decided that there exists a convergenceregion as a given region (ROI) 401.

After the given region (ROI) 401 is determined, the second registrationunit 142 performs a local positioning process (step S1608) for thecomparison destination CT image on the basis of an image of thedetermined given region (ROI) 401. Consequently, the second registrationunit 142 extracts an image of the corresponding region 402 including theposition of a tumor F′ corresponding to the tumor F (image for whichlocal positioning has been performed).

Further, the second registration unit 142 performs an image processingprocess (step S1610) based on a time-dependent variation for the imageof the corresponding region 402 obtained by performing the localpositioning. Further, the second registration unit 142 notifies thedisplay controller 143 of the image of the given region (ROI) 401determined in the comparison source CT image and an image of thecorresponding region 402 extracted from the comparison destination CTimage (where the image has been processed, the image after theprocessing).

The display controller 143 causes the image of the given region (ROI)401 notified from the second registration unit 142 to be displayed in anenlarged scale on the enlarged display screen image on the comparisonsource CT image. Further, the display controller 143 causes the image ofthe corresponding region 402 notified from the second registration unit142 to be displayed in an enlarged scale on the enlarged display screenimage of the comparison destination CT image. Consequently, the imagefor which the image processing has been performed is displayed on theimage of the corresponding region 402 obtained by performing localpositioning.

Now, a flow of processing by the second registration unit 142 in thesecond embodiment is described. FIG. 21 is a second flow chart ofprocessing executed by a second registration unit. It is to be notedthat, from among steps included in the flow chart depicted in FIG. 21,like steps to the steps included in the flow chart of the firstembodiment described hereinabove with reference to FIG. 16 are denotedby like reference characters and overlapping description of these stepsis omitted herein.

At step S2101, the region identification unit 1001 reads out an ROIcandidate 2001 of a size determined in advance and sets the ROIcandidate 2001 to a given position of a comparison source CT image.Here, the region identification unit 1001 sets the ROI candidate 2001 toa scanning starting position of the comparison source CT image (leftupper corner position of the comparison source CT image).

At steps S1602 to S1605, it is decided whether or not there exists aconvergence region on the basis of an image of a region specified by theROI candidate 2001 at the position set at step S2101.

If it is decided at step S2102 that there exists no convergence regionas a result of the processes at steps S1602 to S1605, then therepresentative vector calculation unit 1004 advances the processing tostep S2105.

At step S2105, the region identification unit 1001 decides whether ornot the ROI candidate 2001 is positioned at a terminal end position ofthe comparison source CT image (right lower corner position of thecomparison source CT image). If it is decided that there exists noterminal end position at step S2104, then the processing returns to stepS2101.

In this case, the region identification unit 1001 sets the ROI candidate2001 to a position moved by a given scanning width on the comparisonsource CT image. Then, the region identification unit 1001 executes theprocesses at steps S1601 to S1605.

In this manner, the second registration unit 142 in the presentembodiment searches a given region (ROI) 401 by deciding whether or notthere exists a convergence region while scanning an ROI candidate of agiven size with a given scanning width on a comparison source CT image.

If it is decided that there exists a convergence region as a result ofthe processes at steps S1602 to S1605, then the representative vectorcalculation unit 1004 advances the processing from step S2102 to stepS2103.

At step S2103, the region identification unit 1001 determines the ROIcandidate 2001 at the position at which it is decided that there existsa convergence region as a given region (ROI) 401.

At step S1608, a local positioning process is performed on the basis ofthe image of the given region (ROI) 401 determined at step S2104.Further, if it is decided at step S1609 that an image processing processis to be performed, then an image processing process is performed atstep S1610. Thereafter, the image of the given region (ROI) 401 and theimage for which the image processing process has been performed inregard to the corresponding region (including the translucent layer) areoutputted to the display controller 143. Thereafter, the processingadvances to step S2104.

At step S2104, the region identification unit 1001 decides whether ornot a displaying instruction for a next ROI is received from the displaycontroller 143. It is to be noted that a health care worker can operatethe operation unit 207 to issue an instruction on the parallel displayscreen image 300 to advance to a next ROI. If an operation for issuingan instruction to advance to a next ROI is performed by the health careworker, then the display controller 143 notifies the regionidentification unit 1001 of the instruction.

If it is decided at step S2104 that a displaying instruction of a nextROI is received, then the processing advances to step S2105. On theother hand, if it is decided that a displaying instruction of a next ROIis not received, then the processing by the second registration unit 142is ended.

In this manner, the second registration unit 142 in the presentembodiment scans a comparison source CT image with a given scanningwidth using ROI candidates of a given size. Then, the secondregistration unit 142 determines a region specified at a position atwhich it is decided that there exists a convergence region as a givenregion (ROI) and performs a local positioning process for the givenregion (ROI) on the basis of an image of the given region and thenextracts an image of a corresponding region to perform an imageprocessing process.

Consequently, a health care worker may not perform a process fordesignating a tumor F on a comparison source CT image, and the load onthe health care worker upon diagnosis can be reduced.

Third Embodiment

The second registration unit in the second embodiment determines a givenregion (ROI) 401 by scanning an ROI candidate of a given size on acomparison source CT image with a given scanning width.

In contrast, a second registration unit in the third embodimentdetermines a given region (ROI) 401 while successively changing the sizeand the scanning width of an ROI candidate to be scanned on a comparisonsource CT image. It is to be noted that the second registration unit inthe third embodiment determines, when it scans each ROI candidate witheach scanning width, from among regions specified by positions at whichit is decided that there exists a convergence region, a region in which,for example, the number of difference vectors is equal to or greaterthan a given threshold value as a given region (ROI) 401. In thismanner, with the third embodiment, an optimum given region (ROI) can bedetermined by scanning while the size and the scanning width of an ROIcandidate are successively changed. In the following, the thirdembodiment is described principally of differences thereof from thesecond embodiment described above.

FIG. 22 depict views illustrating a process of scanning a comparisonsource CT image using ROI candidates to determine an ROI. FIG. 22A-1illustrates a manner in which the second registration unit 142 scans ROIcandidate 2001_1 of a comparison source CT image. It is to be notedthat, in the present embodiment, the image processing apparatus 120stores a plurality of ROI candidates in the auxiliary storage unit 204,and the second registration unit 142 successively reads out the ROIcandidates and performs scanning of the ROI candidates on the comparisonsource CT image. FIG. 22A-1 indicates that the ROI candidate 2001_1 isread out first.

FIG. 22A-2 indicates that it is decided as a result of the scanning bythe ROI candidate 2001_1 that a convergence region exists at a givenposition and a region specified by the position is decided as a givenregion (ROI) 401_1. Further, FIG. 22A-3 indicates that it is decided asa result of scanning of the ROI candidate 2001_1 that a convergenceregion exists at a position different from that of the given region401_1 and a region specified by the position is decided as a givenregion (ROI) 401_2.

Meanwhile, FIG. 22A-4 indicates that, after scanning of the ROIcandidate 2001_1 is completed, the second registration unit 142 readsout an ROI candidate 2001_2 of a size different from that of the ROIcandidate 2001_1. The second registration unit 142 performs scanning ofthe comparison source CT image using the newly readout ROI candidate2001_2.

FIG. 22A-5 indicates that it is decided as a result of the scanning ofthe ROI candidate 2001_2 that a convergence region exists at a givenposition and it is decided that a region specified by the position is agiven region 2201_1. Further, FIG. 22A-6 indicates that it is decided asa result of scanning of the ROI candidate 2001_2 there exists aconvergence region at a position different from that of the given region2201_1 and a region specified by the position is decided as a givenregion 2201_2.

In this manner, by performing scanning of individual ROI candidates(2001_1, 2001_2, . . . ), a plurality of regions (401_1, 401_2, 2201_1,2201_2, . . . ) are decided as given regions (ROIs). It is to be notedthat, although, in the description of the example of FIG. 22, a changeof the scanning width is not mentioned, when scanning of the ROIcandidates (2001_1, 2001_2, . . . ) is performed, the scanning isperformed changing the scanning width.

The second registration unit 142 determines a region that satisfies agiven condition from among a plurality of regions (401_1, 401_2, 2201_1,2201_2, . . . ) each decided as an ROI as an ROI.

The example of FIG. 22B illustrates a manner in which the given regions401_1 and 2201_2 from among the plurality of regions (401_1, 401_2,2201_1, 2201_2, . . . ) are determined as ROIs. Consequently, the secondregistration unit 142 in the present embodiment can determine a regionof an optimum size at an optimum position as an ROI.

Now, a flow of processing by the second registration unit 142 in thethird embodiment is described. FIGS. 23 and 24 are a third flow chart ofprocessing executed by a second registration unit.

At step S2301, the region identification unit 1001 sets initial valuesfor the size and the scanning width of an ROI candidate and reads outROI candidates of the set size and scanning width from the auxiliarystorage unit 204.

At step S2101, the region identification unit 1001 sets the ROIcandidate read out at step S2301 to a scanning starting position of acomparison source CT image.

At steps S1602 to S1605, whether or not there exist a convergence regionis decided on the basis of an image of a region specified by the ROIcandidate at the position set at step S2101.

If the representative vector calculation unit 1004 decides at step S2102that there exists no convergence region as a result of the processes atsteps S1602 to S1605, then the processing advances to step S2104.

At step S2104, the region identification unit 1001 decides whether ornot the ROI candidate exists at a terminal end position of thecomparison source CT image (at the right lower end position of thecomparison source CT image). If it is decided at step S2104 that the ROIcandidate does not exist at the terminal end position, then theprocessing returns to step S2101.

In this case, the region identification unit 1001 sets the ROI candidate2001 to a position displaced by a given scanning width (here, scanningwidth equal to the initial value therefor) on the comparison source CTimage and then executes the processes at steps S1602 to S1605 again.

On the other hand, if it is decided that there exists a convergenceregion as a result of the processes at steps S1602 to S1605, then therepresentative vector calculation unit 1004 advances the processing fromstep S2102 to step S2302.

At step S2302, the region identification unit 1001 counts the number ofdifference vectors in a region specified by the position at which it isdecided that there exists a convergence region, and decides whether ornot the number of difference vectors is equal to or greater than a giventhreshold value.

If it is decided at step S2302 that the number of difference vectors isequal to or greater than the given threshold value, then the regionidentification unit 1001 decides the region specified by the ROIcandidate at the position at present as a given region (ROI). Then, theprocessing advances to step S2303.

At step S2303, the region identification unit 1001 retains the regionspecified by the ROI candidate at the position at present as the givenregion (ROI).

On the other hand, if it is decided at step S2302 that the number ofdifference vectors is smaller than the given threshold value, then theregion identification unit 1001 does not decide the region specified bythe ROI candidate at the position at present as the given region (ROI).Then, the processing advances directly to step S2104.

It is to be noted that, since the process when it is decided at stepS2104 that the ROI candidate is not at the terminal end position of thecomparison source CT image is such as described above, description isgiven here of a case in which the ROI candidate is at the terminal endposition of the comparison source CT image.

If it is decided at step S2104 that the ROI candidate is at the terminalend position of the comparison source CT image, then the processingadvances to step S2304. At step S2304, the region identification unit1001 decides whether or not the processes at steps S2101 to S2104 areexecuted with all scanning widths for all of the plurality of ROIcandidates stored in the auxiliary storage unit 204.

If it is decided at step S2304 that there remains an ROI candidate forwhich the processes at steps S2101 to 2104 are not executed, then theprocessing advances to step S2305. Alternatively, if it is decided atstep S2304 that there remains a scanning width that is not used in theprocesses, then the processing advances to step S2305.

At step S2305, the region identification unit 1001 reads out an ROIcandidate for which the processes at steps S2101 to S2104 are notexecuted as yet from among the plurality of ROI candidates stored in theauxiliary storage unit 204, and then the processing returns to stepS2101. Alternatively, the region identification unit 1001 sets ascanning width that is not used for the processes, and then returns theprocessing to step S2101.

In this case, the region identification unit 1001 sets the ROI candidateread out newly at step S2305 to the scanning starting position of thecomparison source CT image. Alternatively, the region identificationunit 1001 sets the ROI candidate read out at present to the scanningstarting position of the comparison source CT image with the scanningwidth set at step S2305.

On the other hand, if it is decided at step S2304 that the regionidentification unit 1001 has executed the processes at steps S2101 toS2104 with all of the scanning widths for all of the plurality of ROIcandidates stored in the auxiliary storage unit 204, then processingadvances to step S2401.

At step S2401 of FIG. 24, the region identification unit 1001substitutes 1 into an ROI counter N. It is to be noted that the ROIcounter is a counter for counting the number of ROIs retained at stepS2303 (regions decided each as given region (ROI)).

At step S2402, the region identification unit 1001 reads out an image ofthe Nth given region (ROI) from among the ROIs retained at step S2303.Here, the image in the first given region (ROI) is read out.

At step S1608, the representative vector calculation unit 1004 and thepositioning unit 1005 perform a local positioning process of theadenocarcinoma. Further, if it is decided at step S1609 that the imageprocessing process is to be performed, then the image processing processis performed at step S1610. Thereafter, the image in the Nth givenregion (ROI) and an image obtained by performing the image processingprocess for the image in the corresponding region 402 (including atranslucent layer) are outputted to the display controller 143,whereafter the processing advances to step S2403.

Consequently, the display controller 143 causes an image of the Nthgiven region (ROI) 401 to be displayed in an enlarged scale on thecomparison source CT image of the parallel display screen image 300, andcauses an image (including the translucent layer) obtained by performingthe image processing process for the image of the corresponding region402 to be displayed in an enlarged scale on the comparison destinationCT image.

At step S2403, the region identification unit 1001 decides whether ornot a displaying instruction of a next ROI is received from the displaycontroller 143. It is to be noted that, on the parallel display screenimage 300, a health care worker can perform an operation for instructingto advance to a next ROI through the operation unit 207. Then, if anoperation for instructing to advance to a next ROI is performed by ahealth care worker, then the display controller 143 notifies the regionidentification unit 1001 of the instruction.

If it is decided at step S2403 that a displaying instruction of a nextROI is received, then the processing advances to step S2404.

The region identification unit 1001 calculates the ROI counter N=N+1 atstep S2404 and then returns the processing to step S2402, at which itreads out an image of the Nth given region (ROI). Here, an image of thesecond given region (ROI) is read out.

In this manner, at steps S2402 to S2404, a local positioning process andan image processing process are successively performed in accordancewith an instruction from the health care worker on the basis of an imageof the ROI retained at step S2303. Consequently, an image of the givenregion 401 and an image obtained by performing an image processingprocess for an image of the corresponding region 402 are successivelydisplayed in an enlarged scale on the parallel display screen image 300.

On the other hand, if it is decided at step S2403 that a displayinginstruction of a next ROI is not received, then the processing by thesecond registration unit 142 is ended.

As described above, the second registration unit 142 in the presentembodiment performs scanning while the size and the scanning width of anROI candidate are successively changed on the comparison source CTimage. Further, the second registration unit 142 in the presentembodiment determines a region specified at a position at which it isdecided that a given condition is satisfied from among the positions atwhich it is decided that there exists a convergence region as a givenregion (ROI).

Consequently, the health care worker may no more perform a process fordesignating a tumor F on a comparison source CT image. Therefore, theload on the health care worker upon diagnosis can be reduced and, upondiagnosis, an optical ROI can be determined.

Fourth Embodiment

The image processing apparatus according to the first to thirdembodiments are described as apparatus that display an image (includinga translucent layer) of a corresponding region for which an imageprocessing process is performed by the second registration unit in anenlarged scale on a parallel display screen image.

In contrast, an image processing apparatus in the fourth embodimentchanges, when an image (including a translucent layer) of acorresponding region for which an image processing process is performedby the second registration unit is to be displayed in an enlarged scaleon a parallel display screen image, the display mode in response to anoperation by a health care worker.

This is because, by an image processing process by the secondregistration unit, a plurality of layers are superposed on an image in acorresponding region and there is the possibility that the image of thecorresponding region may become less easy to observe to the health careworker. In the following, the fourth embodiment is described principallyof differences thereof from the first embodiment described hereinabove.

FIG. 25 is a view illustrating a relationship among processing contentsof a diagnosis support unit in an image processing apparatus, operationcontents of a health care worker, and displaying contents of a paralleldisplay screen image. FIG. 25 corresponds to FIG. 4 depicted inconnection with the first embodiment described hereinabove (viewillustrating processing contents and so forth later than globalpositioning).

As depicted in FIG. 25, if a health care worker designates a position ofa tumor F on a displayed comparison source CT image, then an image ofthe given region (ROI) 401 is displayed in an enlarged scale on anenlarged display screen image on the comparison source CT image.Further, on the comparison destination CT image, an image for which animage processing process has been performed (including a translucentlayer) is displayed in an enlarged scale on an enlarged display screenimage on an image of the corresponding region 402 obtained by performinga local positioning process.

If a health care worker performs various operations for an imagedisplayed in an enlarged scale on an enlarged screen image on acomparison destination CT image using a pointer 2501, then the displaycontroller 143 changes the display mode of the image displayed in anenlarged scale on the enlarged display screen image on the comparisondestination CT image in response to the various operations by the healthcare worker.

FIG. 26 depict views illustrating a manner in which a display mode of animage of an enlarged display screen is changed by a display controller.FIG. 26A illustrates a manner in which a translucent layer on whichdifference vectors are displayed is superposed on an image 1400 of acorresponding region 402 and another translucent layer colored atpartition groups 1102 at positions corresponding to the positions of thedifference vectors is superposed further.

FIG. 26B illustrates a manner in which a health care worker moves apointer 2501 toward the image 1400 of the corresponding region 402 onwhich the plurality of translucent layers are superposed.

FIG. 26C illustrates a manner in which, when the health care workermoves the pointer 2501 toward the center of the image 1400, on thetranslucent layer on which the difference vectors are displayed, thedisplayed difference vectors are retracted to the outside of aconvergence region. Further, on the translucent layer colored at thepartition groups 1102 at the positions corresponding to the positions ofthe difference vectors, the colored partition groups 1102 are retractedto the outside of the convergence region.

As depicted in FIG. 26C, the display controller 143 moves the displayeddifference vectors such that the positions of the difference vectors aremoved from the positions depicted in FIG. 26B to the outer side asviewed from a position at which the distal ends of the vectors areconcentrated on the translucent layer. Further, the display controller143 moves the colored partition groups 1102 to similar positions.

As a result, the partition groups 1102 on which the difference vectorsare displayed and colored are not superposed on the convergence regionof the image 1400 of the corresponding region 402, and therefore, it isfacilitated for the health care worker to observe the convergenceregion. It is to be noted that, if the health care worker moves thepointer 2501 away from the image 1400, then the displayed differencevectors retracted to the outside of the convergence region are returnedto the original positions as depicted in FIG. 26A. Further, thepartition groups 1102 retracted to the outside of the convergence regionare returned to the original positions.

It is to be noted that the display controller 143 may change, when tomove displayed difference vectors to the outside of the convergenceregion on the translucent layer, the color of the difference vectors toa display color different from that of the difference vectors before themovement. Similarly, the display controller 143 may change, when to movethe partition groups 1102 at the positions corresponding to thepositions of the difference vectors to the outside of the convergenceregion on the translucent layer, the color such that the colors beforeand after the change are different from each other.

It is to be noted that, in the example of FIG. 26, a case is describedin which displayed difference vectors (or colored partition groups 1102)are moved in response to the position of the pointer 2501. However, themovement of displayed difference vectors is not limited to this. Forexample, where the pointer 2501 is used to issue an instruction todisplay a further enlarged image, displayed difference vectors (orcolored partition groups 1102) may be moved under a condition that thecenter of the enlarged display remains within the convergence region.

Now, a flow of processing executed by the display controller 143 in thepresent embodiment is described. FIG. 27 is a flow chart of processingexecuted by a display controller.

At step S2701, the display controller 143 decides whether or not a layeron which difference vectors (or colored partition groups 1102) aredisplayed is superposed on an image 1400 of a corresponding region 402.If it is decided at step S2701 that a layer on which difference vectors(or colored partition groups 1102) are displayed is not superposed, thenthe processing advances to step S2708.

On the other hand, if it is decided at step S2701 that a layer on whichdifference vectors (or colored partition groups 1102) are displayed issuperposed, then the processing advances to step S2702. At step S2702,the display controller 143 calculates a density of difference vectors ina convergence region on the image 1400 of the corresponding region 402.For example, the display controller 143 acquires a range of theconvergence region calculated by the convergence region decision unit1003 and a number of difference vectors included in the convergenceregion. Further, the display controller 143 calculates a density of thedifference vectors in the convergence region (“overlapping ratio”calculated from the number of difference vectors superposed in theconvergence region) on the basis of the acquired range of theconvergence region and the acquired number of difference vectors.

At step S2703, the display controller 143 decides whether or not thedensity calculated at step S2702 is higher than a given threshold value(given ratio). If it is decided at step S2703 that the density is equalto lower than the given threshold value, then the processing advances tostep S2708.

On the other hand, if it is decided at step S2703 that the density ishigher than the given threshold value (namely, a number of differencevectors greater than a given ratio are placed in a region smaller than agiven extent), then the processing advances to step S2704. At stepS2704, the display controller 143 decides whether or not the pointer2501 enters the image 1400 of the corresponding region 402.

If it is decided at step S2704 that the pointer 2501 enters the image1400 of the corresponding region 402, then the processing advances tostep S2706. At step S2706, the display controller 143 retracts thedisplay (difference vectors or colored partition groups 1102) on thelayer superposed on the image 1400 once to the outside of theconvergence region.

On the other hand, if it is decided at step S2704 that the pointer 2501does not enter the convergence region, then the processing advances tostep S2705. At step S2705, the display controller 143 decides, when thehealth care worker instructs to perform display of the image 1400 in afurther increased scale using the pointer 2501, whether or not thecenter of the enlarged display is within the convergence region.

If it is decided at step S2705 that the center of the enlarged displayis within the convergence region, then the processing advances to stepS2706. On the other hand, if it is decided at step S2705 that the centerof the enlarged display is not within the convergence region, then theprocessing advances to step S2707.

At step S2707, if the display on the layer superposed on the image 1400of the corresponding region 402 is retracted to the outside of theconvergence region, the display controller 143 returns the display toits original display position. It is to be noted that the displaycontroller 143 maintains the current display position if the display onthe layer superposed on the image 1400 is not retracted to the outsideof the convergence display (in the case the display on the originaldisplay position).

At step S2708, the display controller 143 decides whether or not theprocessing being executed is to be ended. It is to be noted that thecase in which the process being executed is to be ended is a case inwhich the display of the image 1400 of the corresponding region 402displayed at present is ended. The case in which the display of theimage 1400 of the corresponding region 402 being displayed at presentis, for example, a case in which a given region different from the givenregion displayed at present is designated, another case in which acomparison source CT image different from the comparison source CT imagebeing displayed at present is displayed or a like case.

If it is decided at step S2708 that the process being executed is not tobe ended, then the processing returns to step S2701. On the other hand,if it is decided at step S2708 that the processing being executed is tobe ended, then the processing is ended.

As described above, in the display controller 143 in the presentembodiment, a display on a layer superposed on an image of acorresponding region is moved to the outside of a convergence region inresponse to a pointer operation by a health care worker. Consequently,the health care worker can reduce difficulty in observing a convergenceregion (portion to be diagnosed) on an image of a corresponding region.

Fifth Embodiment

In the first embodiment described hereinabove, the image processor 1006visualizes a tendency of a change of a tissue based on a change of atumor on the basis of a calculated distribution of difference vectors(number of difference vectors per unit area). However, the method forthe visualization of a tendency of a change of a tissue is not limitedto this. For example, a speed of change of a tissue may be calculatedand visualized on the basis of the magnitude and the elapsed time of thecalculated difference vectors.

Further, in the third embodiment described hereinabove, the secondregistration unit 142 determines a region in which the number ofdifference vectors is equal to or greater than a given threshold valuefrom among regions in which it is decided that there exists aconvergence region as an ROI. However, the determination method of anROI is not limited to this. Any other determination method may be usedif it can determine a region in which the degree of convergence of atissue is high is an ROI.

Further, in the third embodiment described above, an image processingprocess is executed after all ROIs are determined on a comparison sourceCT image. However, an image processing process may otherwise be executedevery time an ROI is determined.

Further, in the first to fourth embodiments described hereinabove, animage of a given region (ROI) is displayed in an enlarged scale on anenlarged display screen image. However, an image of a given region (ROI)may be displayed in an enlarged scale on a comparison source CT imagelike a loupe view.

Further, in the first to fourth embodiments described hereinabove, aninstruction for image processing is accepted after global positioning iscompleted. However, an instruction for image processing may be acceptedotherwise after an image of a corresponding image is displayed.

Further, in the first to fourth embodiments described hereinabove, a CTimage is displayed. However, the embodiments disclosed herein may beapplied also to a case in which a medical image other than a CT image(for example, a magnetic resonance imaging (MRI) image) is displayed.

It is to be noted that the embodiments disclosed herein is not limitedto the configurations described hereinabove in connection with theembodiments and the configurations may have some other elements combinedtherewith. The configurations of the embodiments can be modified invarious manners without departing from the spirit and scope of thepresent embodiments and can be applied appropriately in accordance withan application form thereof.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the inventionand the concepts contributed by the inventor to furthering the art, andare to be construed as being without limitation to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although the embodiments of the presentinvention have been described in detail, it should be understood thatthe various changes, substitutions, and alterations could be made heretowithout departing from the spirit and scope of the invention.

What is claimed is:
 1. A non-transitory computer readable storage mediumstoring a program that causes a computer to execute a processcomprising: obtaining a first image of a chest and a second image of thechest scanned after the first image; specifying a plurality ofdifference vectors indicating a changes of a plurality of positions in alung between the first image and the second image; determining whether achange in the lung occurs, the change being a change indicated byspecified difference vectors, in the plurality of difference vectors,toward a specified position in the lung; transforming the second imageto adjust the plurality of positions in the second image to theplurality of positions in the first image based on the plurality ofdifference vectors far from the specified position by a predetermineddistance or more; and outputting the transformed second image on adisplay device.
 2. The non-transitory computer readable storage mediumaccording to claim 1, wherein an area corresponding to the change in thelung is displayed on the display device.
 3. The non-transitory computerreadable storage medium according to claim 2, wherein the specifiedposition is displayed on the display device in a different manner fromother parts of the lung.
 4. The non-transitory computer readable storagemedium according to claim 1, wherein the plurality of difference vectorstoward the specified position is displayed so as to be superposed on thetransformed second image.
 5. The non-transitory computer readablestorage medium according to claim 4, wherein the plurality of differencefar from the specified position by the predetermined distance or more isvectors indicating the change of the lung that is caused by breathingand a heart beating, is excluded from the specified vectors.
 6. Thenon-transitory computer readable storage medium according to claim 1,wherein the change in the lung indicates that alveoli in the lung arecollapsed by an adenocarcinoma.
 7. An apparatus for image processing,the apparatus comprising: a memory; and a processor coupled to thememory and configured to: obtaining a first image of a chest and asecond image of the chest scanned after the first image; specifying aplurality of difference vectors indicating a changes of a plurality ofpositions in a lung between the first image and the second image;determine whether a change in the lung occurs, the change being a changeindicated by specified difference vectors, in the plurality ofdifference vectors, toward a specified position in the lung; transformthe second image to adjust the plurality of positions in the secondimage to the plurality of positions in the first image based on theplurality of difference vectors far from the specified position by apredetermined distance or more; and output the transformed second imageon a display device.
 8. A method for image processing, the methodcomprising: obtaining a first image of a chest and a second image of thechest scanned after the first image; specifying a plurality ofdifference vectors indicating a changes of a plurality of positions in alung between the first image and the second image; determining whether achange in the lung occurs, the change being a change indicated byspecified difference vectors, in the plurality of difference vectors,toward a specified position in the lung; transforming the second imageto adjust the plurality of positions in the second image to theplurality of positions in the first image based on the plurality ofdifference vectors far from the specified position by a predetermineddistance or more; and outputting the transformed second image on adisplay device.